Activity recognition techniques are generally known in which a motion measurement apparatus such as an acceleration sensor or a gyro-sensor is mounted on a human body to measure and recognize activities of a subject (for example, refer to Ling Bao, Stephen S. Intille, “Activity Recognition from User-Annotated Acceleration Data”, In Second International Conference on Pervasive Computing, pp. 1-17, 2004).
However, while conventional activity recognition techniques extract features corresponding to activities of a subject from sensor data (activity data) measured by and obtained from a motion measurement apparatus to recognize activities of the subject, the techniques are not designed to recognize activities in adaptation to each individual and problematically result in a decline in recognition accuracy.
More specifically, even when the same activity is performed by two different individuals, differences in physique such as gender and age, individual differences such as dominant hands and dominant legs, and differences such as deviations in sensor mounting positions or the like generally result in activity data that differs between the individuals and, consequently, different features extracted from such activity data. In a learning stage, since a recognition instrument must be created so as be capable of recognizing such different activity data and features as the same activity, the accuracy of the recognition instrument is relatively low. In addition, in a recognition stage, the possibility of an erroneous recognition made on a target individual having a different physique from a subject during learning is relatively high.